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---
language:
- en
- ja
- pt
- es
- ko
- ar
- tr
- th
- fr
- id
- ru
- de
- fa
- it
- zh
- pl
- hi
- ur
- nl
- el
- ms
- ca
- sr
- sv
- uk
- he
- fi
- cs
- ta
- ne
- vi
- hu
- eo
- bn
- mr
- ml
- hr
- no
- sw
- sl
- te
- az
- da
- ro
- gl
- gu
- ps
- mk
- kn
- bg
- lv
- eu
- pa
- et
- mn
- sq
- si
- sd
- la
- is
- jv
- lt
- ku
- am
- bs
- hy
- or
- sk
- uz
- cy
- my
- su
- br
- as
- af
- be
- fy
- kk
- ga
- lo
- ka
- km
- sa
- mg
- so
- ug
- ky
- gd
- yi
tags:
- Twitter
- Multilingual
license: "apache-2.0"
mask_token: "<mask>"
---
# TwHIN-BERT: A Socially-Enriched Pre-trained Language Model for Multilingual Tweet Representations
[](http://makeapullrequest.com)
[](https://arxiv.org/abs/2209.07562)
This repo contains models, code and pointers to datasets from our paper: [TwHIN-BERT: A Socially-Enriched Pre-trained Language Model for Multilingual Tweet Representations](https://arxiv.org/abs/2209.07562).
[[PDF]](https://arxiv.org/pdf/2209.07562.pdf)
[[HuggingFace Models]](https://huggingface.co/Twitter)
### Overview
TwHIN-BERT is a new multi-lingual Tweet language model that is trained on 7 billion Tweets from over 100 distinct languages. TwHIN-BERT differs from prior pre-trained language models as it is trained with not only text-based self-supervision (e.g., MLM), but also with a social objective based on the rich social engagements within a Twitter Heterogeneous Information Network (TwHIN).
TwHIN-BERT can be used as a drop-in replacement for BERT in a variety of NLP and recommendation tasks. It not only outperforms similar models semantic understanding tasks such text classification), but also **social recommendation** tasks such as predicting user to Tweet engagement.
## 1. Pretrained Models
We initially release two pretrained TwHIN-BERT models (base and large) that are compatible wit the [HuggingFace BERT models](https://github.com/huggingface/transformers).
| Model | Size | Download Link (🤗 HuggingFace) |
| ------------- | ------------- | --------- |
| TwHIN-BERT-base | 280M parameters | [Twitter/TwHIN-BERT-base](https://huggingface.co/Twitter/twhin-bert-base) |
| TwHIN-BERT-large | 550M parameters | [Twitter/TwHIN-BERT-large](https://huggingface.co/Twitter/twhin-bert-large) |
To use these models in 🤗 Transformers:
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained('Twitter/twhin-bert-large')
model = AutoModel.from_pretrained('Twitter/twhin-bert-large')
inputs = tokenizer("I'm using TwHIN-BERT! #TwHIN-BERT #NLP", return_tensors="pt")
outputs = model(**inputs)
```
<!-- ## 2. Set up environment and data
### Environment
TBD
## 3. Fine-tune TwHIN-BERT
TBD -->
## Citation
If you use TwHIN-BERT or out datasets in your work, please cite the following:
```bib
@article{zhang2022twhin,
title={TwHIN-BERT: A Socially-Enriched Pre-trained Language Model for Multilingual Tweet Representations},
author={Zhang, Xinyang and Malkov, Yury and Florez, Omar and Park, Serim and McWilliams, Brian and Han, Jiawei and El-Kishky, Ahmed},
journal={arXiv preprint arXiv:2209.07562},
year={2022}
}
``` |